Class %DeepSee.PMML.Model.GeneralRegression

abstract class %DeepSee.PMML.Model.GeneralRegression extends AbstractModel

Parameters	Properties	Methods	Queries	Indices	ForeignKeys	Triggers
2	6	4

Summary

Properties
ContrastMatrices	PPMatrix	ParamMatrix	Parameters
Predictors	Tracing

Methods
%AddToSaveSet	%ClassIsLatestVersion	%ClassName	%ConstructClone
%DispatchClassMethod	%DispatchGetModified	%DispatchGetProperty	%DispatchMethod
%DispatchSetModified	%DispatchSetMultidimProperty	%DispatchSetProperty	%ExecuteModel
%ExecuteModelDeepSee	%Extends	%GetDataClassName	%GetInputClassName
%GetInputObject	%GetName	%GetOutputClassName	%GetParameter
%GetPredictedField	%GetPredictedFieldInfo	%IsA	%IsModified
%New	%NormalizeObject	%ObjectModified	%OriginalNamespace
%PackageName	%RemoveFromSaveSet	%SerializeObject	%SetModified
%ValidateObject	CalculateXBeta

Parameters

• parameter TRACEWEIGHTTYPE = "add";

How the weighting info in this models trace output needs to be interpreted. Either of:

"none": only rank info available

"tree": trace info is a sequence of tree nodes leading to the prediction

"add": additive contributions (total score might be a function of this sum)

"multiply": multiplicative contributions (total score might be a function of this product)

Properties

• property ContrastMatrices [ MultiDimensional ];

..ContrastMatrices(predictor, row, col)

• property PPMatrix [ MultiDimensional ];

Based on ..PPMatrix(paramName, predictorName) = value

• property ParamMatrix [ MultiDimensional ];

Based on ..ParamMatrix(targetValue, paramName) = beta

• property Parameters [ MultiDimensional ];

Based on ..Parameters(paramName) = label

• property Predictors [ MultiDimensional ];

Based on and ..Predictors(paramName) = [factor|covariate]

Methods

• abstract method CalculateXBeta(ByRef pObservation As %DeepSee.PMML.ModelInput, Output pXBeta, Output pBestTarget As %String = "", Output pBestScore As %Double = "", pAddZero As %String = "") as %Status

Class Reference IRIS for UNIX 2024.1.2
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